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---
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language: en
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license: apache-2.0
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tags:
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- financial-sentiment
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- sentiment-analysis
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- finance
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- nlp
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- transformers
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datasets:
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- zeroshot/twitter-financial-news-sentiment
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metrics:
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- accuracy
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- f1
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model-index:
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- name: financial-sentiment-improved
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results:
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- task:
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type: text-classification
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name: Financial Sentiment Analysis
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dataset:
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name: Twitter Financial News Sentiment
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type: zeroshot/twitter-financial-news-sentiment
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metrics:
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- type: accuracy
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value: 0.847
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- type: f1
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value: 0.845
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---
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# financial-sentiment-improved
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This model is a fine-tuned version of DistilBERT for financial sentiment analysis. It has been trained on financial news and social media data to classify text into three sentiment categories: Bearish (negative), Neutral, and Bullish (positive).
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## Model Performance
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- **Accuracy**: 0.847
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- **F1 Score**: 0.845
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## Usage
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```python
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import torch
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tokenizer = AutoTokenizer.from_pretrained("codealchemist01/financial-sentiment-improved")
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model = AutoModelForSequenceClassification.from_pretrained("codealchemist01/financial-sentiment-improved")
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def predict_sentiment(text):
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inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
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with torch.no_grad():
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outputs = model(**inputs)
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predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
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labels = ["Bearish", "Neutral", "Bullish"]
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predicted_class = torch.argmax(predictions, dim=-1).item()
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confidence = predictions[0][predicted_class].item()
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return {
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"label": labels[predicted_class],
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"confidence": confidence
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}
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# Example
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result = predict_sentiment("The stock market is showing strong growth today")
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print(result)
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```
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## Training Details
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This model was trained using advanced techniques including:
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- Balanced dataset sampling
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- Custom loss functions
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- Learning rate scheduling
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- Early stopping
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## Intended Use
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This model is designed for financial sentiment analysis tasks, including:
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- Social media sentiment monitoring
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- News sentiment analysis
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- Market sentiment tracking
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- Financial document analysis
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